Interactive Data Visualization

Row

car failure analysis

Failure

Car failure in US

1624

Labor Cost

Massachusetts

21

California

200

Texas

293

Florida

168

Row

Failure by State

Top States

Failure month vs Failure Channel

Row

Scatter Plot of Month Vs Mileage

Box Plot of Top State

Map

Map

Data Table

Pivot Table

Summary

Column {data-width = 100}

Max Failure Month

23

Average Labor Cost

242.92

Average Mileage at Failure

20578.67

Column

Report

  • This is a report in 1624 car failure.

  • The average labor cost was 242.9180111.

  • The average material cost was 179.3948276.

This report was generated on April 15, 2020.

About Report

Created by: Abhishek Jaglan

---
title: "AJ's Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    social: ["twitter","facebook","menu"]
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(ggvis)
```

```{r}
data <- read.csv("~/Desktop/VehicleFailure.csv")
```

```{r}
mycolors <- c("blue","#FFC12","darkgreen","darkorange")
```

Interactive Data Visualization
==================================

Row
-----------------------------

### car failure analysis

```{r}
valueBox(paste("Failure"),
         color = "warning")
```

### Car failure in US

```{r}
valueBox(length(data$State),
         icon = "fa-user")
```

### **Labor Cost**

```{r}
gauge(round(mean(data$lc),
            digits = 2),
            min = 0,
            max = 350,
            gaugeSectors(success = c(0,150),
                         warning = c(150,240),
                         danger = c(240,350),
                         colors = c('green','yellow','red')))
```

### Massachusetts

```{r}
valueBox(sum(data$State=="MA"),
         icon = 'fa-building')
```         

### California
```{r}
valueBox(sum(data$State=="CA"),
         icon = 'fa-building')
```

### Texas

```{r}
valueBox(sum(data$State=="TX"),
         icon = 'fa-building')
```

### Florida

```{r}
valueBox(sum(data$State=="FL"),
         icon = 'fa-building')
```

Row
----------

### Failure by State

```{r}
 p1 <- data %>%
         group_by(State) %>%
         summarise(count = n())%>%
        plot_ly(x = ~State,
                y = ~count,
                color = rainbow(51),
                type = 'bar')%>%
  layout(xaxis = list(title = "Failure By State"),
         yaxis = list(title = 'count'))
p1
```

### Top States
```{r}
p2 <- data %>%
          group_by(State)%>%
          summarise(count = n()) %>%
          filter(count > 50) %>%
          plot_ly(labels = ~State,
                  values = ~count,
                  marker = list(colors = mycolors))%>%
          add_pie(hole = 0) %>%
          layout(xaxis = list(zeroline = F,
                              showline = F,
                              showticklabels = F,
                              showgrid = F),
                 yaxis = list(zeroline = F,
                              showline = F,
                              showticklabels = F,
                              showgrid = F))
p2
```

### Failure month vs Failure Channel

```{r}
p3 <- plot_ly(data,
              x  = ~fm,
              y = ~Mileage,
              text = paste("FM;",data$fm,
                           "Mileage:",
                           data$Mileage),
              type = "bar") %>%
      layout(xaxis = list(title = "FM"),
             yaxis = list(title = "Failure Mileage"))
p3
```

Row
---------------------

### Scatter Plot of Month Vs Mileage

```{r}
p4 <- plot_ly(data,x=~fm) %>%
        add_markers(y = ~Mileage,
                    text = ~paste("Mileage: ",Mileage),
                    showlegend = F)%>%
        add_lines(y = ~fitted(loess(Mileage ~ fm)),
                  name = "Loess Smoother",
                  color = I("#FFC125"),
                  showlegend = T,
                  line = list(width = 5)) %>%
        layout(xaxis = list(title = "Month"),
               yaxis = list(title = "Mileage"))
p4
```

### Box Plot of Top State

```{r}
data %>%
        group_by(State) %>%
        ggvis(~State, ~lc, fill = ~State) %>%
        layer_boxplots()
```

Map
=============

### Map
 
```{r}
car <- data %>%
  group_by(State) %>%
  summarise(total = n())
car$State <- abbr2state(car$State)
highchart()%>%
  hc_title(text = "Car Failure in US") %>%
  hc_subtitle(text = "Source: Vehiclefailure.csv") %>%
  hc_add_series_map(usgeojson,car,
                    name = "State",
                    value = "total",
                    joinBy = c("woename","State")) %>%
  hc_mapNavigation(enabled = T)
```

Data Table
===========

```{r}
datatable(data,
          caption = "Failure Data",
          rownames = T,
          filter = "top",
          options = list(pageLength = 25))
```

Pivot Table 
======

```{r}
rpivotTable(data,
            aggregatorName = "Count",
            cols = "fm",
            rows = "State",
            rendererName = "Heatmap")
```

Summary{data-orientation=columns}
===========================================

Column {data-width = 100}
--------------

### Max Failure Month

```{r}
valueBox(max(data$fm),
         icon = "fa-user")
```

### Average Labor Cost

```{r}
valueBox(round(mean(data$lc),
               digits = 2),
         icon = "fa-area-chart")
```

### Average Mileage at Failure 

```{r}
valueBox(round(mean(data$Mileage),digits = 2),
         icon = "fa-area-chart")
```

Column
----------------------

Report

* This is a report in `r length(data$fm)` car failure. 

* The average labor cost was `r mean(data$lc)`.

* The average material cost was `r mean(data$mc)`.

This report was generated on `r format(Sys.Date(), format = "%B %d, %Y")`.

About Report
===========================

Created by: Abhishek Jaglan